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Classical Reaction Barriers in DFT: An Adiabatic-Connection Perspective.

Andrew M Wibowo-Teale1, Bang C Huynh1, Trygve Helgaker2

  • 1School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, U.K.

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|December 23, 2024
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Summary
This summary is machine-generated.

This study introduces a new method to visualize reaction barriers in density-functional theory. It reveals that exchange functionals determine barrier accuracy and correlation functionals determine barrier shape, offering insights into improving computational chemistry models.

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Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • Density-Functional Theory

Background:

  • Classical reaction barriers are crucial for understanding chemical reactions.
  • Density-functional theory (DFT) is a widely used method for calculating these barriers.
  • Errors in DFT functionals can lead to inaccuracies in predicted reaction barriers.

Purpose of the Study:

  • To introduce a novel 'reaction adiabatic-connection integrand' for visualizing reaction barriers.
  • To analyze functional-driven errors in DFT reaction barriers.
  • To separate and understand the contributions of exchange and correlation functionals to barrier heights.

Main Methods:

  • Developed the 'reaction adiabatic-connection integrand' () based on the density-fixed adiabatic connection.
  • Calculated reference using Lieb maximizations at the coupled-cluster level of theory.
  • Compared reference with approximate from common exchange-correlation functionals using coordinate scaling on coupled-cluster densities for five chemical reactions.

Main Results:

  • The reaction barrier height is directly visualized as the area under the vs λ plot.
  • Accuracy of is solely determined by the accuracy of the exchange functional.
  • The shape of is solely determined by the correlation functional, explaining variations in hybrid functional performance.

Conclusions:

  • Simply increasing exact exchange in hybrid functionals is not a reliable way to improve reaction barriers.
  • Accurate correlation functionals are essential for capturing the correct shape of and improving barrier predictions.
  • The presented numerical data can guide the development of more accurate correlation functionals.